Predictive Control Based Energy Management Research Articles

A Stochastic Model Predictive Control Based Energy Management Approach for Microgrids with Electric Vehicles

A Stochastic Model Predictive Control Based Energy Management Approach for Microgrids with Electric Vehicles

Lifetime-Optimized Energy Management Strategy for Fuel Cell Unmanned Aircraft Vehicle Hybrid Power System

To improve the durability of the fuel cell unmanned aircraft vehicle (UAV) hybrid power system, a model predictive control based energy management strategy (EMS) with minimum energy loss is presented in this paper. The proposed algorithm can effectively reduce the internal loss and power stress of the hybrid power system, which can help to extend fuel cell lifespan. Specifically, a calculation formula with observable parameters is proposed to predict the internal loss of the battery in both the charging and discharging mode. Besides, to increase the hybrid UAV adaptability under complex flying conditions, the fuel cell operating trajectory is also defined in the algorithm to increase the system dynamic performance and efficiency. To verify the effectiveness of the strategy, the hardware-in-the-loop (HIL) experimental test platform is built. Compared to the conventional equivalent consumption minimum strategy (ECMS), the proposed strategy can effectively alleviate the voltage degradation of the fuel cell and decrease the energy loss of the battery. The proposed strategy can help to contribute to the applications of fuel cell hybrid power system in the aviation fields.

Learning assisted column generation for model predictive control based energy management in microgrids

Model predictive control is an effective approach for microgrid energy management. However, the main downside of such method is its expensive online computational cost, which is not amenable to most practical microgrid implementations. To address this issue, we propose a deep neural network assisted column generation approach that can accelerate the solution procedure of model predictive control. In each iteration, our approach leverages different deep neural networks to predict the optimal solutions of all the subproblems in column generation, which can accelerate the computation of all the subproblems and the entire process of column generation. The pre-existing knowledge of the microgrid is also exploited to guarantee the feasibility of the neural network outputs using multi-parametric programming theory. The numerical results show that our approach leads to a reduction in computational time of approximately 50% in a medium-sized microgrid compared with the full mixed integer solution based on traditional branch and bound method, while the optimality loss is only 0.02% in terms of operating costs.

Model Predictive Control Based Energy Management System Literature Assessment for RES Integration

Over the past few decades, the electric power industry evolved in response to growing concerns about climate change and the rising price of fossil fuels. The usage of renewable energy sources (RES) rose as a remedy for these problems. The increased penetration of RES in the existing generation system increased the need for an intelligent energy management system (EMS) so that the system can operate in any possible circumstances. Many sectors of society, including the education sector, are working to realize the importance of this sustainable energy system. This paper reviews the process of selecting an efficient control technique for continuous power flow from different RES to meet the load demand requirement using an enhanced model predictive control (MPC)-based EMS framework. This EMS is a software platform to provide fundamental support services and applications to deliver the functionality needed for the effective operation of electrical generation and transmission facilities to ensure adequate security of energy supply at minimum cost. The centralized EMS with technical objectives focusing on power quality and seamless power flow can be achieved through dynamically enhanced MPC.

Model Predictive Control Based Energy Management Strategy of Series Hybrid Electric Vehicles Considering Driving Pattern Recognition

This paper proposes an energy management strategy for a series hybrid electric vehicle based on driving pattern recognition, driving condition prediction, and model predictive control to improve the fuel consumption while maintain the state of charge of the battery. To further improve the computational efficiency, the discretization and linearization of the model is conducted, and the MPC problem is transferred into a quadratic programming problem, which can be solved by the interior point method effectively. The simulation is carried out by using Matlab/Simulink platform, and the simulation results verify the feasibility of the condition prediction method and the performance of the proposed method. In addition, the predictive control strategy successfully improves the fuel economy of the hybrid vehicle compared with the rule-based method.

Energy saving analysis in electrified powertrain using look-ahead energy management scheme

Connectivity enabled energy management strategies rely on various look-ahead information sources to predict a future disturbance trajectory in order to optimize the operation of the vehicle. The look-ahead data and their combinations contribute to the accuracy of the predicted disturbance trajectory and hence, enhance the optimality of the energy management strategy. This paper presents a systematic methodology to evaluate the utility of look-ahead data provided by on-board sensors and connectivity. The look-ahead data is first used in a parametric model-based velocity predictor to generate accurate future velocity trajectory of the vehicle on a route. The predicted velocity is used to inform the model predictive control based energy management system. The utility of look-ahead data in improving the optimality of energy management system is evaluated in a simulation study with a class 6 series-hybrid electric powertrain. The simulation study is conducted on two routes calibrated with real world traffic and road data to demonstrate the utility of the proposed methodology. The results of the study shows energy saving benefits saturate as the levels of look-ahead data increase and a dependence of the route characteristics.

Robust Nonlinear Economic MPC Based Management of a Multi Energy Microgrid

In this paper, the nonlinear economic model predictive control based energy management of a multi-energy microgrid is presented. The microgrid is composed of the district heating system with a cogeneration power plant supplying both the heat and the electricity demand of residential buildings with photovoltaic sources. Furthermore, access to liberalized heat and electricity market is assumed. This kind of environment requires consideration of the economical aspects, the technical aspects, the safety/quality of supply, and consumer preferences. The major contributions introduced in the presented approach are the following. A nonlinear formulation is adopted to capture the nonlinear thermal models and the nonlinear algebraic model of the heat supply network. The second major contribution is the economic character of the objective following the assumption of a liberalized market environment. Such formulation accounts for the specifics of the energy market organization. Finally, the stochastic nature of the planning problem is introduced through a practical robust formulation. The results obtained on a representative case study clearly show the benefits of the proposed approach compared to the traditional energy management approach.

Impact of Incorporating Disturbance Prediction on the Performance of Energy Management Systems in Micro-Grid

The design and implementation of appropriate advanced control strategies is a key factor for the effective integration of micro-grids into the electrical network. In view of this, the study proposes an Adaptive Model-Based Horizon Control technique in the bid to addressing issues related to the Energy Management System in micro-grid operations. The main objective of the energy management system is to balance energy generation and demand through energy storage, so as to optimize the operation of the micro-grid with high penetrations of renewable energy sources. This paper further investigates the impacts of considering the prediction of disturbances on the performance of the Energy Management System based on the adaptive model predictive control algorithm in order to improve the operating costs of the micro-grid with hybrid-energy storage systems. The adaptive model predictive control algorithm solves the energy optimization problem in a renewable energy-based micro-grid with various types of energy storage systems that exchange energy with the host grid. More so, this optimization problem is resolved at each sampling period in order to determine the minimum running costs while satisfying demand and taking into account technical and physical constraints. The simulation results under different conditions have demonstrated how the use of an adaptive model predictive control based energy management system can enhance micro-grid operation, provided there is effective forecasting, and consequently minimized the running operating costs of micro-grid. More so, it is evident in the cost function, J, obtained from the three scenarios conducted, that the perfect knowledge of the disturbance prediction is essential for effective micro-grid operations.

Implementation and evaluation of real-time model predictive control for load fluctuations mitigation in all-electric ship propulsion systems

Electrification is a clear trend for both commercial and military ship development. Shipboard load fluctuations, such as propulsion-load fluctuations and pulse power loads, can significantly affect power system reliability. In order to address this issue, this paper explores a real-time model predictive control based energy management strategy for load fluctuation mitigation in all-electric ships. A battery combined with ultra-capacitor hybrid energy storage system (HESS) is used as a buffer to compensate load fluctuations from the shipboard network. In order to implement the proposed real-time MPC-based energy management strategy on a physical testbed, three special efforts have been made to enable real-time implementation: a specially tailored problem formulation, an efficient optimization algorithm and a multi-core hardware implementation. Given the multi-frequency characteristics of load fluctuations, a filter-based power split strategy is developed as a baseline control to evaluate the proposed MPC. Compared to the filter-based strategy, the experimental results show that the proposed real-time MPC achieves superior performance in terms of enhanced system reliability, improved HESS efficiency, long self-sustained time, and extended battery life. The bus voltage variation and hybrid energy storage losses can be reduced by up to 38% and 65%, respectively.

Optimal Energy Use in a Light Weight Hydraulic Hybrid Passenger Vehicle

In this study we present a procedure for the design and implementation of a control strategy to optimize energy use within a light weight hydraulic hybrid passenger vehicle. The hydraulic hybrid utilizes a high pressure accumulator for energy storage which has superior power density than conventional battery technology. This makes fluid power attractive for urban driving applications in which there are frequent starts and stops and large startup power demands. A dynamic model of a series hydraulic hybrid powertrain is presented along with the design of a model predictive control based energy management strategy. Model predictive control was chosen for this study because it uses no future information about the drive cycle in its design. This increases the flexibility of the controller allowing it to be directly applied to a variety of drive cycles. Using the model predictive framework, a holistic view of the powertrain was taken in the design of the control strategy, and the impact of each actuator’s efficiency on overall efficiency was evaluated. A hardware-in-the-loop experiment using an electro-hydraulic powertrain testbed was then used to validate the dynamic model and control performance. Through a simulation study in which each actuator’s efficiency was given varying levels of priority in the objective function, it was found that overall system efficiency could be improved by allowing for small sacrifices in individual component performance. In fact, the conventional wisdom of using the additional degrees of freedom within a hybrid powertrain to optimize engine efficiency was found to yield the lowest overall powertrain efficiency. In this work we present a rigorous framework for the design of an energy management strategy. The design method improves the powertrain’s operational efficiency by finding the best balance between optimizing individual component efficiencies. Furthermore, since the design of the control strategy is built upon an analysis of individual components, it can be readily extended to other architectures employing different actuators.